Imprecise Survival Signature Approximation Using Interval Predictor Models

authored by: Jasper Behrensdorf, Matteo Broggi, Michael Beer
Abstract: This paper presents a novel technique for the approximation
of the survival signature for very large systems.
In recent years, the survival signature has seen promising
applications for the reliability analysis of critical infrastructures.
It outperforms traditional techniques by allowing for complex
modelling of dependencies, common causes of failures and
imprecision. However, as an inherently combinatorial method, the
survival signature suffers greatly from the curse of dimensionality.
Computation for very large systems, as needed for critical
infrastructures, is mostly infeasible. New advancements have
applied Monte Carlo simulation to approximate the signature instead
of performing a full evaluation. This allows for significantly
larger systems to be considered. Unfortunately, these approaches
will also quickly reach their limits with growing network size
and complexity. In this work, instead of approximating the full
survival signature, we will strategically select key values of the
signature to accurately approximate it using a surrogate radial
basis function network. This surrogate model is then extended to
an interval predictor model (IPM) to account for the uncertainty
in the prediction of the remaining unknown values. In contrast to
standard models, IPMs return an interval bounding the survival
signature entry. The resulting imprecise survival signature is then
fed into the reliability analysis, yielding upper and lower bounds
on the reliability of the system. This new method provides a
significant reduction in numerical effort enabling the analysis of
larger systems where the required computational demand was
previously prohibitive.
Organisation(s): Institute for Risk and Reliability
Faculty of Civil Engineering and Geodetic Science
CRC 871 Regeneration of Complex Capital Goods
Type: Conference contribution
Pages: 506-511
No. of pages: 6
Publication date: 2023
Publication status: Published
Peer reviewed: Yes
Electronic version(s): https://doi.org/10.1109/SSCI52147.2023.10371939 (Access: Closed)